IIC Journal of Innovation - 1 -

Causal Analytics in IIoT – AI That Knows What

Causes What, and When

Authors:

Dr. John Galloway Founder Au Sable johnjgalloway01@gmail.com Pieter van Schalkwyk Chief Executive Officer XMPro pieter.vanschalkwyk@xmpro.com Dann Marian Engineering and Projects Manager dann.marian@iinet.net.au

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INTRODUCTION

Finding the “because” behind certain

business or operations events has always

been a key part of any engineering,

maintenance or operations manager’s job in

industrial businesses. “The First Stage

compressor failed because…” or “the supply

tank ran dry because…” are common

phrases in maintenance and operations

departments in industrial businesses.

Finding the “because” traditionally relies on

experienced engineers that can interpret

event, contextual and temporal data to

deduce the likelihood of specific factors

causing others in either a negative or

positive way.

Knowing the real root causes of events is

critical to resolving problems rather than

continuously dealing with the symptoms. It

resulted in popular, formalized approaches

such as “Root Cause Analysis,” or RCA as it is

generally known. The challenge is that there

are often multiple causal factors for these

events, and finding the one “root cause”

may not always be possible. Understanding

other causal factors that may influence the

outcome of industrial processes and the

behavior of equipment need to be

considered.

Au Sable, in collaboration with XMPro,

developed an algorithmic, artificial

intelligence-based, approach for “Reliable

Causal Analytics” (rCA) in industrial IoT

applications. This article demonstrates:

It is possible to perform Reliable Causal

Analytics using industrial IoT data and

Artificial Intelligence (AI) to determine

causality of business and operational

events such as equipment failure or

operational issues

How Reliable Causal Analytics provides

data-driven decision support for

traditional Root Cause Analysis

approaches

The approach to embed this causal

analytics methodology in IoT Process

Management software to be able to

perform this in a repeatable and

automated manner.

rCA is the result of many years of research

and application of causal analytics in real-

world scenarios. Through this, Au Sable

developed rCA that enables cause and effect

relationships to be identified from sensor-

driven data and made known to the analyst

(e.g. wear on part #105 has causally

impacted the performance of device #65

with a causal coefficient of 0.86), as well as

correlation relationships in the data.

This means:

the risk of making false decisions about

what were, or will be predictively, the

causal drivers of an effect is reduced,

and

the potential for costly or disastrous

mistakes is thereby reduced.

This article provides background on

traditional Root Cause Analysis and the

evolution of Causal Analytics. It

demonstrates how to automate the

analytics to scale with an IoT Process

Management platform and how it is applied

in an industrial application. It provides a

practical example of Reliable Causal

Causal Analytics in IIoT – AI That Knows What Causes What, and When

IIC Journal of Innovation - 3 -

Analytics (rCA) applied to a floating

production storage and offloading (FPSO)1

vessel for an Oil & Gas company.

Crude oil, gas and water from the reservoir

are separated on board the FPSO. Oil is

stored on the facility in six pairs of tanks,

before export to trading tankers. The vessel

is designed to store 1.4 million barrels of oil

and processes approximately 170,000

barrels of oil per day (bopd).

Example Floating Production Storage and Offloading Vessel

Au Sable’s rCA has functioned in intelligence,

defense and anti-terrorism applications for

many years. The solution described in this

article is the combination of advanced IoT

Process Management software from XMPro

and the rCA AI software from Au Sable.

1 Floating production storage and offloading https://en.wikipedia.org/wiki/Floating_production_storage_and_offloading

2 http://asq.org/learn-about-quality/root-cause-analysis/overview/roots-of-root-cause.html

3 Wilson, Paul F.; Dell, Larry D.; Anderson, Gaylord F. (1993). Root Cause Analysis: A Tool for Total Quality Management.

Milwaukee, Wisconsin: ASQ Quality Press. pp. 8–17. ISBN 0-87389-163-5.

4 Adapted from https://en.wikipedia.org/wiki/Root_cause_analysis (classification)

ROOT CAUSE ANALYSIS BASED ON

CORRELATION DOESN’T WORK IN

THE IOT ERA

Industrial RCA Background

Formal Root Cause Analysis for industrial

applications started with the Total Quality

Management (TQM)2 movement advocated

by Deming in Japan in the late 1980’s and

early 1990’s.

Paul Wilson et al3 described the root cause

analysis process for Quality Management in

detail during the TQM era. “Root cause

analysis is a method of problem-solving used

for identifying the root causes of faults or

problems. A factor is considered a root cause

if removal thereof from the problem-fault-

sequence prevents the final undesirable

outcome from recurring; whereas a causal

factor is one that affects an event's outcome,

but is not a root cause. Though removing a

causal factor can benefit an outcome, it does

not prevent its recurrence with certainty.”

Even though root cause analysis formally

originated in TQM, it finds many applications

in industrial environments:4

Safety-based Root Cause Analysis arose from the fields of accident analysis and occupational safety and health.

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Production-based Root Cause Analysis has roots in the field of quality control for industrial manufacturing.

Process-based Root Cause Analysis, a follow-on to production-based RCA, broadens the scope of RCA to include business processes.

Failure-based Root Cause Analysis originates in the practice of failure analysis as employed in engineering and maintenance.

Systems-based Root Cause Analysis has emerged as an amalgam of the preceding schools, incorporating elements from other fields such as change management, risk management and systems analysis.

Root Cause Analysis became popular as an

approach to methodically identify and

correct the root causes of events instead of

addressing symptomatic results of these

events. The objective of root cause analysis

is to prevent problem recurrence. Some

popular root cause analysis techniques

include “Five Whys” and Cause and Effect

(Fishbone) diagrams. These techniques rely

on human interpretation of event

information and data and require

experienced practitioners to conduct the

analysis. It is often limited to a few critical

production assets as the manual process is

time-consuming and laborious. Wilson’s

distinction between root causes and other

causal factors provides some guidance on

the application of causal analytics in an IoT

context for this article. Traditional

techniques focused only on finding the root

causes through manual review. Modern

techniques such as rCA described in this

article, combined with IoT data and

advances in AI, enable engineers to not only

assess root causes, but also find other causal

factors. These causal factors may not lead to

equipment or process failure but may still

impact equipment or process performance.

Recent advances in cloud computing and AI

provide the necessary infrastructure to

analyze event data for IoT and other sources

at massive scale. This means analysts can

have a more expansive view of causal events

rather than a reductionist view where the

scope of an analysis is limited to what a

human can process.

MOTIVATION FOR DATA-DRIVEN,

RELIABLE CAUSAL ANALYTICS

There are three main reasons to find a

reliable, data-driven approach to finding

root causes and causal factors for equipment

failure and operational performance in

industrial environments:

Aging workforce and a large number of experienced engineers retiring soon

Complexity of equipment, making it harder to troubleshoot

Inaccuracy of Root Cause Analysis

Retiring Workforce

With a retiring workforce in many industrial

sectors, the experience needed to conduct

meaningful RCAs is decreasing. As much of

the traditional approaches rely on

observational analysis, the number of

experienced engineers that can provide

reliable analysis is fast reducing.

According to a January 2017 assessment by

the US Department of Energy, 25% of US

employees in electric and natural gas utilities

Causal Analytics in IIoT – AI That Knows What Causes What, and When

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will be ready to retire within 5 years5. The US

Department of Labor also estimates that the

average age of industry employees is now

over 50 and up to half of the current energy

industry workforce will retire within 5-10

years.6

Manual RCA requires the combination of a

rigorous methodology, fault analysis

technology and experience to evaluate the

possible causes of business events such as

equipment failure, quality problems or

safety incidents. Much of the expertise

needed will be lost with the retiring

workforce. A data-driven, algorithmic

approach provides a viable replacement for

the experience of people to determine

causal relationships between business

events.

Complexity of Industrial Equipment

As industrial equipment becomes

increasingly sophisticated 7 and more

complex, the ability to perform diagnostics

becomes increasingly more difficult. As

equipment becomes more complex and

sophisticated, the number or combinations

and permutations of potential causal factors

for certain events increases exponentially. It

follows a similar pattern to Metcalfe’s law8

5 U.S. Department of Energy, Quadrennial Energy Review (QER) Task Force report second installment titled “Transforming the

Nation’s Electricity System.” Chapter V: Electricity Workforce of the 21st-Century: Changing Needs and New Opportunities.

January 2017. Retrieved from https://energy.gov/epsa/initiatives/quadrennial-energy-review-qer

6 U.S. Department of Labor Employment and Training Administration “Industry Profile – Energy.” Retrieved from

https://www.doleta.gov/brg/indprof/energy_profile.cfm

7 Challenges To Complex Equipment Manufacturers: Managing Complexity, Delivering Flexibility, and Providing Optimal Service

http://www.oracle.com/us/solutions/046249.pdf

8 Metcalfe's law https://en.wikipedia.org/wiki/Metcalfe%27s_law

9 The problem with root cause analysis http://qualitysafety.bmj.com/content/26/5/417

for telecommunication devices that states

“the effect of a telecommunications

network is proportional to the square of the

number of connected users of the system

(n2)”.

Metcalfe’s law, now also used in economics

and business management, provides some

quantification of the impact of the increasing

complexity of equipment to troubleshoot

potential causal relationships between

operational events.

Inaccuracy of Root Cause Analysis

Root Cause Analysis gained popularity in

industrial and other sectors such as

healthcare. One of the main challenges that

emerged centers around the fact that it

requires facilitation and analysis by people

who can process only limited amounts of

information. People are also susceptible to

opinions and organizational influences such

as politics. Peerally 9 et al describe the

problem with Root Cause Analysis with these

8 main challenges:

The unhealthy quest for “the” root cause

Questionable quality of RCA investigations

Political hijack

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Poorly designed or implemented risk controls

Poorly functioning feedback loops

Disaggregated analysis focused on single organizations and incidents

Confusion about blame

The problem of many hands

Many of these are as a result of the

subjective nature of the people doing

analysis and can be addressed with a more

objective, data-driven approach. People

can’t process all the potential data sources

of event and contextual information.

Modern advances in data, stream and event

processing address some of that challenge

and AI provides a means to make sense of

the data at scale. It removes the reliance on

the subjective nature of human analysis and

opens the opportunity to analyze fact-based

information at scale to derive insights.

The unhealthy quest for “the” root cause

further describes a challenge that can be

better addressed with an algorithmic

approach to Root Cause Analysis. Peerally

states that “the first problem with Root

Cause Analysis is its name. By implying—

even inadvertently—that a single root cause

(or a small number of causes) can be found,

the term ‘root cause analysis’ promotes a

flawed reductionist view.”

An algorithmic approach often provides

more potential causal factors, their

10 How does business analytics contribute to business value? https://onlinelibrary.wiley.com/doi/pdf/10.1111/isj.12101

11 The Age of Analytics: Competing in a data-driven world

https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Analytics/Our%20Insights/The%20age%2

0of%20analytics%20Competing%20in%20a%20data%20driven%20world/MGI-The-Age-of-Analytics-Full-report.ashx

relationship to each other and the strength

(causal coefficient) of the relationships. It

offers additional insights into events and

often finds causation that may be

counterintuitive to the views of the people

that do it manually. An algorithmic approach

also provides repeatability and scale. It will

analyze the IoT and contextual data in a

consistent way that is independent of the

person performing the analysis.

USING CAUSAL ANALYTICS TO

PERFORM ALGORITHMIC ROOT

CAUSE ANALYSIS

Correlation is Not Causation

In this era of big data, it is commonly said

that data analytics is a prime driver of value

to enterprises10. This is true, but only if the

analytics performed across the data are well

grounded methodologically and perform

well and efficiently to derive the value.

Big data creates big and complex data

volumes. This is of limited value however, if

it is not accompanied by the best available

analytics to enable the most valuable,

accurate and reliable decisions to occur11 .

Hence, there is an increasing requirement

for the analytics component in industrial IoT

solutions to be fast, reliable and accurate to

identify the problems and opportunities and

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ensure that such problems are addressed

correctly and urgently.

Correlation of events and systems is often a

starting point for problem-solving in

industrial environments but “correlation is

not causation”12. Correlation helps to point

the way, helps indicate what might be

candidate causative or driving factors for

some particular effect yet keeping in mind

that correlation is simply a measure of

association not causation.

Introductory statistics courses tell us that it

is not possible to prove causation unless one

conducts an experiment whereby treatment

and control groups are randomized.

This is totally correct but is just not feasible

to conduct an experiment in 99% of real-

world situations. Algorithmic methods have

a probabilistic and contributory approach –

spurred on by big data’s need for

empirically-based data-driven decisions – to

answering questions about what caused

what or will. For example, a causal

coefficient of 0.83 of X as a causative

influence on Y, does not mean that X is

necessarily the sole cause of Y (there may be

multiple causes) nor does it always cause Y.

X is identified however as a contributory

cause of Y. Similarly, smoking is a

contributory cause of lung cancer; it is not

12 Correlation does not imply causation https://en.wikipedia.org/wiki/Correlation_does_not_imply_causation

13 Attaining IoT Value: How To Move from Connecting Things to Capturing Insights

https://www.cisco.com/c/dam/en_us/solutions/trends/iot/docs/iot-data-analytics-white-paper.PDF

14 The IoT’s Potential for Transformation http://e.huawei.com/en-

sa/publications/global/ict_insights/201703141505/focus/201703141643

the sole cause, nor does it always cause the

effect.

Correlations can be misleading. Valuable

results and insights are often found, but the

correlation methods upon which decisions

are made mean that risks are inherent and

could lead to mistaken or sub-optimal

decision-making and outcomes.

The chief analytics tool of most industrial IoT

analytics vendors is correlation. Most

sensor-driven data (IoT and machine-

generated logs) is analyzed using a proven

but older form of statistical methods (even

when operating within a machine learning

framework). Correlational methods are the

dominant form of analytics.

Some examples from IoT vendor

publications and websites demonstrate this

approach:

1. Cisco (Attaining IoT Value): …enable the company’s customers to perform real-time data correlation and, as a result, quickly react to irregularities13

2. Huawei (‘The IoT's Potential for Transformation’): …enables correlation-based process and productivity improvements.14

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3. ThingWorx's capabilities make it possible for users to correlate data, deriving powerful insights…15

4. Siemens PLM: …quantitative statistical relationships to real-life usage, called customer correlation16

5. Industrial Internet Consortium: …common issue in IIoT systems is correlating data between multiple sensors and process control states17

Correlational methods are established as

powerful aids to decision-making as is

witnessed in the rise of platforms that

provide the capability. Correlations often

vary such that at a given time one entity and

another may be positively related and at

other times only weakly related or not at all.

There is no fact-based causal coefficient that

describes the strength of potential causal

relationships.

The lack of stability in correlations indicates

complexity in the relationships and the

presence of a dynamical system (common in

IIoT). This results in variability according to

the system state and nonlinearity in system

behavior. It means that traditional statistical

methods, correlation included, have

limitations for obtaining precise analytics

and improved decision making about

performance in IIoT.

15 A survey of IoT cloud platforms https://www.sciencedirect.com/science/article/pii/S2314728816300149

16 Customer Correlation Durability Methodology

https://www.plm.automation.siemens.com/en/products/lms/engineering/customer-correlation.shtml

17 Industrial Analytics: The Engine Driving the IIoT Revolution https://www.iiconsortium.org/pdf/Industrial_Analytics-

the_engine_driving_IIoT_revolution_20170321_FINAL.pdf

The result is that an observed correlation

over time may or may not be coincidental;

or, the observed correlation (and any

implied causation) may be the result of one

or more third-party variables (hidden

confounders), e.g. another variable that

influences two events that are seemingly

correlated. An example of this may be ice

cream sales and boating accidents that are

correlated, but both are affected by summer

temperatures, and so a causal inference

would be spurious. In this example summer

temperature is causal, but one may

incorrectly infer causation that an increase

in ice cream sales leads to boating accidents

due to the high correlation factor. More

humorous examples of these erroneous

correlations can be found at Spurious

Correlations.

Mathematically-based causal analytics

attempts to improve on correlation for

causality identification.

The Evolution of Causal Analytics

CAUSALITY FOR REAL-WORLD APPLICATIONS

It is well accepted that causation cannot be

proven statistically unless one conducts an

experiment with randomization to control

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for spurious relationships 18 19 , which is

simply not practicable in most real-world

situations. The position of the authors is that

correlational methods have served well and

are proven to provide useful insights, but are

nonetheless prone to producing spurious

relationships and hence mistaken

decisions.20 21

As noted earlier, causality research has been

undertaken to develop different

probabilistic methods and approaches for

identifying cause and effect relationships in

non-experimental or ‘observational’ data.

Causal analytics evolved over the past few

decades from academic studies to practical

solutions such as rCA. A stumbling block

historically in reaching this goal has been to

devise causal algorithms that produce

reliable and accurate results for commercial

and government application.

ADVANCES IN CAUSAL ANALYTICS AND THE

DEVELOPMENT OF RELIABLE CAUSAL ANALYTICS (RCA)

In the 1980s, mathematical advances by

Judea Pearl22 from UCLA showed that causal

relationships can be represented from data

in terms of probabilities and led him later to

declare that “causality has been

mathematized”. The mathematization was

perhaps a little premature, but Pearl’s

18 https://us.sagepub.com/sites/default/files/upm-binaries/14289_BachmanChapter5.pdf

19 http://www.statisticssolutions.com/establishing-cause-and-effect/

20 https://hbr.org/2015/06/beware-spurious-correlations

21 https://en.wikipedia.org/wiki/Spurious_relationship

22 Judea Pearl https://en.wikipedia.org/wiki/Judea_Pearl

23 Clive Granger https://en.wikipedia.org/wiki/Clive_Granger

outstanding work led him to be awarded in

2012 the industry’s equivalent of the Nobel

Prize, the Turing award, for advances in both

machine learning and causality.

Problems remained however, e.g. how to

identify a causal relationship when unknown

delays occur between cause and effect. And,

what are termed hidden confounders, were

difficult to identify and control for. Earlier,

the work of Weiner (1950s) laid the basis for

several information-theoretic measures of

causality (and for well-known data

compression algorithms).

A landmark innovation was that of Clive

Granger 23 , awarded a Nobel prize for

developing a test of causality: X is said to

cause Y, if the past values of X contain

information that helps predict future values

of Y, above and beyond the information

contained in past values of Y – graphically:

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Researchers extended this framework, e.g.

to allow for analysis of multiple time series

generated by nonlinear models, for lagging

the cause and effect variables and for causal

graphical models for better handling of

latent variables.

Transfer Entropy 24 25 (TE) is a later

implementation of the principle that causes

must precede and predict their effects. TE

improves on Granger in that it directly caters

for nonlinear interactions and helps

minimize problems of noisy data. TE is a

model-free and non-parametric measure of

directed information flow from one variable

to another.

24 Transfer Entropy https://en.wikipedia.org/wiki/Transfer_entropy

25 Transfer entropy between multivariate time series https://www.sciencedirect.com/science/article/pii/S1007570416305020

26 Progress in Root Cause and Fault Propagation Analysis of Large-Scale Industrial Processes

https://www.hindawi.com/journals/jcse/2012/478373/

The application of TE to empirical analytics

has been substantial in areas of biomedicine

and climate science. However, further

developments were needed to help

overcome shortcomings related to

unreliability and a lack of accuracy 26 . Au

Sable’s work on improving the reliability of

TE, combined with other Au Sable

proprietary algorithms, have led to the

development of an algorithmic approach to

causal analytics that can process IoT event

data and provide reliable results. This means

that Causal Analytics can now be applied to

real-world scenarios with non-experimental

data.

Figure 1: Granger causality test

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These real-world applications involve

methods that take into account the

complexity of systems (thereby including

analytics of system machine and log data).

The inter-dependencies and dimensionality

of many IIoT system devices mean that

identifying their behavior (causal and

otherwise) can be extremely difficult

depending on the magnitude and nature of

the couplings. One variable may be found to

be a driver of another, but not alone. The

multiple influences that have an impact on a

particular variable must be teased out, such

as the timings, state-dependencies and

multi-dimensionality of other influences that

impact an ‘effect’ of interest, such as a

decrease in pressure or rise in temperature.

These are identified as part of the rCA

process for IIoT.

This approach has led to an area of causal

research from a dynamical systems

perspective. A dynamical system is one in

which a function describes the time

dependence of a point in a geometrical

space.27 28 29 30 A dynamical systems course

at Harvard states that the methods have a

focus on the behavior of systems described

by ordinary differential equations.

Application areas “…are diverse and

multidisciplinary, ranging over areas of

applied science and engineering, including

biology, chemistry, physics, finance, and

27 https://en.wikipedia.org/wiki/Dynamical_system

28 https://en.wikipedia.org/wiki/Dynamical_systems_theory

29 https://mathinsight.org/dynamical_system_idea

30 http://math.huji.ac.il/~mhochman/research-expo.html

31 https://scholar.harvard.edu/siams/am-147-nonlinear-dynamical-systems

industrial applied mathematics."31 This is a

fairly recent set of developments and

especially with respect to incorporating AI

and machine learning where these

algorithms can be applied to IIoT data at

scale.

Automating rCA in Industrial IoT

Applications

Although the rCA approach can be employed

on an ad-hoc basis by an analyst, the real

benefits come from automating the rCA AI

analysis as part of an IoT process. The rCA

function can be executed based on trigger

events such as data changes or exceptions.

The rCA software and algorithms are

embedded in the functions library of the

XMPro IoT Process platform for IIoT

applications.

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In this example, event data is ingested from

their Honeywell® historian and

contextualized with asset data from their

IBM Maximo® EAM system. Further context

is provided from operational data stores.

This information is passed to the rCA Causal

Analytics AI function that creates the causal

coefficient matrix and other outputs

described later in the article.

This automated, process-based approach

ensures repeatability, consistency and that it

can be done at scale for a large number of

assets in a process stream. The automated

process can process and analyze much larger

volumes of IoT data than human RCA

analysts. In the FPSO example different

analyses are automated at different time

intervals such as daily for high impact

equipment and weekly or monthly for other

areas. This is configurable by the end users

and ad hoc analysis can also be performed.

CUSTOMER EXAMPLE: RCA IN OIL

AND GAS PROCESSING

Background to the Application of rCA in Oil

& Gas

The example demonstrates how rCA can

enable an FPSO to optimize production and

productivity as well as predict and avoid

incidents which threaten health, safety,

environment, community and financial

Figure 2: XMPro IoT Process Stream for rCA

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outcomes. The initial field study project was

aimed at three key objectives:

EFFICIENT OPERATIONS AND MAINTENANCE

This project will drive down the costs of

reduced or lost production caused by

unplanned failures. Other operational

efficiency gains will be achieved by reducing

the risks of environmental impact caused by

operational failure and the risks to personnel

safety caused by breaches of operational

standards. Furthermore, the costs of asset

maintenance will be reduced and the

capability of diagnosing asset health in

remote and challenging operating

environments is increased.

SAFETY AND SOCIAL LICENSE TO OPERATE

Equipment failure and/or an unsafe work

environment can potentially result in harm

to humans or the environment, ultimately

increasing operational risk and impacting an

organization’s social license to operate.

This solution will assist in providing a safe

production environment. In addition,

through to the inbuilt predictive analytics,

further eliminate operational risks which

could impact the social license to operate if

undetected and left uninvestigated and

unaddressed.

ENABLING EFFECTIVE COLLABORATION

Traditionally there exists a significant divide

between the operational technology (OT) in

heavy asset sectors like Oil & Gas and the

information technology (IT) arena. Not only

are they typically separated by physical,

geographical and network constraints, they

are also generally isolated philosophically.

The innovative solution and integrated

application suite enables interoperability of

data feeds from sensors and devices, with

the associated referential information from

the asset registry and maintenance

framework. It combines data from both IT

and OT and this new information provides

insights that can be shared collaboratively

between OT, IT and Operations. It makes

new levels of operational excellence,

collaboration and sustained productivity

improvements possible.

The project mirrored an upstream oil and gas

process flow including value-added services

at each stage of the supply chain leveraging

real-time IoT big data, machine learning and

artificial intelligence.

Going beyond the obvious elements that

cause an interruption to production, rCA is

used to find root causes and inter-

dependency which may be overlooked or

not realized with current technology. This

will enable the operations team onboard the

FPSO to keep it in production without

interruption for long periods and, when

down, to be repaired and brought on-stream

faster.

Most importantly, these improvements

reduce the risk of events that impact the

safety of all personnel on the FPSO and

protect the environment on the vessel and in

the geographic vicinity.

Project Background

The FPSO plant had experienced occasional

periods of operational instability. These

were largely unexplained, yet some

significant and costly problems resulted. It

was particularly challenging to identify the

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actual cause(s) of the problems. Routine

correlational methods of analysis, such as

traditional RCA, had been applied but

provided the operators with only limited

assistance.

The project was conducted in 2 phases. In

the initial phase the FPSO operator wanted

to validate the algorithms through an initial

manual analysis before automating the

process in phase 2.

Au Sable’s rCA system used sensor-driven

and machine data covering a defined period

where these events occurred as the basis for

the analysis. The rCA analysis discovered

cause-effect relationships that were not

previously known by the engineers and that

helped to identify and address the real root

causes of the problem. The results of the rCA

analysis in phase 1 provided new insights

into causal relationships that were

previously not considered in the human

analysis process. This is of significant value

and benefit to the operations and

engineering teams of the Oil & Gas customer

operating the FPSO and it provided the

support to automate the process in phase 2.

The example data shown is that of the phase

1 analysis that provided the insights and

confidence in the output to support the

decision to automate the process for a larger

data set and additional equipment.

rCA on the FPSO

During phase 1 the analysis was done

manually to validate the model and establish

the workflow for the automated steps in

phase 2.

The analysis approach in phase 2 consists of

three main process steps:

Ingest data: Real-time feeds of operational data are collected from intelligent equipment (e.g. submersible pumps, etc.) and from the Honeywell Historian through XMPro’s Listener integration connectors that stream the data to the analysis step;

Perform analysis: The streaming data from the previous step is passed to the rCA algorithms in the XMPro rCA Functions connector where the analysis is performed; and

XMPro Action Agents provide reports and actions on the results of the analysis that identify real causes and not just symptoms of production outages.

Data from sensors at locations across the

plant operations were mapped to locations

on a process flow diagram (PFD) and are

shown as red circles in Figure 3. This provides

a familiar visual reference for the engineers

of the physical process and the data from the

different sources.

Causal Analytics in IIoT – AI That Knows What Causes What, and When

IIC Journal of Innovation - 15 -

The FPSO plant engineers provided one

month’s data at one-minute intervals from

sensors at the above locations. The rCA AI

algorithm processed the data to identify a

limited number of cause and effect

relationships between the equipment or

devices in Figure 3 where there is a high

causation coefficient. This is derived from a

proprietary causal effects matrix. The causal

effects matrix tends to be sparse with a

much small number than in a correlation

matrix, typically about 10-15% for the same

data.

The output from the causal effects matrix

that provided invaluable insight for the

engineers is a graph (Figure 4) that ranks

causal relationship based on causal

coefficient and the confidence level in the

causal relationship. It identifies those

relationships with high causality and high

confidence at a glance and engineers can use

this information to map it back to the

physical process.

It is easy to spot events that have a high

causal coefficient with high confidence

levels and focus on the meaning and impact

of these insights.

Figure 3: Locations of sensors (red circles) on a process diagram of plant operations

Causal Analytics in IIoT – AI That Knows What Causes What, and When

- 16 - June 2018

An enhanced PFD diagram, Figure 5, shows

the top casual relationships between the

equipment/devices overlaid on the process

flow diagram. This approach connects the

analytical model with the physical process

Figure 4: Chart of the top cause and effect relationships

Figure 5: Overlay of causal relationships on a process flow diagram – showing causal coefficient values plus

confidence levels in brackets

Causal Analytics in IIoT – AI That Knows What Causes What, and When

IIC Journal of Innovation - 17 -

model for the engineers who can interpret

the results.

This diagram shows, for example the causal

relationship and confidence level, in

brackets, of the condenser coil pressure and

the flash vessel pressure (nodes I and J) that

correspond to the chart in Figure 4.

The plant engineers were most interested in

examining the main relationships, those with

the strongest measure casual coefficients

and higher levels of confidence.

Figure 6 provides a causal coefficient view in

a traditional graph that removes the

contextual bias that an engineer may have.

This view enabled the engineers to see

causal relationships without the physical

process relationships. It triangulated some

of their findings and observations from the

chart and process flow diagram views.

Findings and Observations from the Phase 1

Analysis

The chart in Figure 4 provided the most

insight to the FPSO operators. The initial

objective to validate the rCA algorithm was

accomplished with physical evidence to

support the outputs of the rCA analysis. The

following three examples describe some of

that validation process.

The high casual coefficient of the condenser

coil pressure and the flash vessel pressure

made sense from an engineering perspective

as it is part of the design. It was expected to

have a high causal relationship and it did.

This proved that the rCA algorithm

performed as expected.

The causal relationship between the

condenser coil pressure and the dewpoint

(middle of the chart) was not obvious prior

Figure 6: The top cause and effect relationships shown as a directed graph

Causal Analytics in IIoT – AI That Knows What Causes What, and When

- 18 - June 2018

to the analysis and it turned out that it had

an impact on operations. It led to further

investigations by the engineering team.

The third example relates to the causal

relationship at the bottom of the chart. It has

a high causal coefficient, but a low

confidence level for ambient temperature to

measure dewpoint. In a physical, isolated

system the 2 should not be related from an

engineering perspective. One of the

engineers found the cause after

investigation. The dewpoint measurement

sensor is exposed to full sun when the vessel

sits in a certain orientation, which affected

the dewpoint measurement and led it to

track the ambient temperature as a measure

of exposure to sunlight. This in turn affected

the efficiency of operations as the rate of lift-

gas being dehydrated was affected.

The three examples provided enough

evidence to proceed with phase 2 to

automate the process for scheduled time

intervals and to include additional data

points. One of the main benefits of the

automated process is that the FPSO

engineers can perform the root cause

analysis without the assistance of a data

scientist to manually perform the analysis.

This is an important requirement for the

FPSO operator to deploy rCA at scale. The

nature of causal analytics requires a

complete re-analysis if any of the causes

found in previous analyses were addressed

or changed. There are often unintended

consequences of making changes that only

show up in a new analysis. These analyses

need to be performed in a consistent

manner which is facilitated by the

automation of the process. Phase 2 is

underway at the time of writing this article

and the benefits of the automation process

will described in a future article by the

authors.

The use case illustrated that an additional

dimension of understanding can be added to

the analysis and troubleshooting of

Industrial IoT problems. The results are

reported in the above formats and able to be

extended to additional desired forms of

output.

CONCLUSION

Most IoT platform vendors use traditional

statistical methods based on correlation and

regression for their analytics functionality.

These are proven to work but also have

limitations that restrict their applicability to

quickly and accurately identify causative

factors that did, or will, cause some effect.

This means the decision making may be sub-

optimal or even inaccurate.

rCA adds a different and complementary

dimension of IoT data analysis and decision

making. rCA is a breakthrough technology

that brings new methods for the application

of cause and effect analytics to real-world

industrial problems. By adding a new and

powerful dimension of analytics, it enables

users to refine their decision-making, reduce

risks, improve safety and reliability, and

reduce costs and equipment downtime.

The culmination of this “Reliable Causal

Analytics” approach is the ability for the

solution to enable operators to more

completely understand:

What caused what (and when)

What will cause what (and when)

Causal Analytics in IIoT – AI That Knows What Causes What, and When

IIC Journal of Innovation - 19 -

What could/should be done to avoid…

Which processes could/should be changed to improve going forward

Continuous improvement of processes

Future work and research is planned to

extend rCA to other use cases and IIoT

applications. Interested parties are invited to

submit use cases for consideration.

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